Known devices may be helpful in providing in-vivo sensing, for example, using imaging techniques, systems and/or methods. One of the uses of such devices may involve visual detection and/or identification of objects or tissues that may be or include in-vivo anomalies (e.g., pathologies). Autonomous in-vivo sensing devices, e.g., swallowable or ingestible capsules, may move through a body lumen, and sense, monitor or otherwise obtain data as they move along/through the gastrointestinal (“GI”) system or other body lumens. An autonomous in-vivo sensing device may include, for example, an imager for obtaining images of a body cavity or lumen, such as the GI tract. An autonomous in-vivo sensing device may also include an optical system, a light source, a controller and optionally a transmitter and an antenna. Some of these devices transfer image data wirelessly.
Although high quality data may be produced and provided by in-vivo sensing devices, e.g., high resolution images or video streams, analyzing the data may be costly and/or time consuming. For example, identifying an anomaly in a video stream produced by an in-vivo device traveling through the GI tract may require hours since the entire video may have to be examined, possibly by a physician or trained person or healthcare professional. It would be, therefore, beneficial to detect anomalies in imaged tissues automatically.
U.S. Pat. No. 8,913,807 (hereinafter, “the '807 patent, which is incorporated herein by reference in its entirety) describes a system and method for detecting anomalies in a color image of a tissue of the GI system by segmenting the color image into valid tissue zones and non-valid tissue zones. According to the '807 patent, a valid tissue zone may be a zone within an image whose pixels have intensities that are higher than a predetermined intensity reference and are relatively uniform, e.g., their intensities or gray levels do not change significantly across the zone. For example, these pixels may have a relatively small variance in terms of intensity or gray level. Then, one or more anomaly regions may be searched for in each valid tissue zone based on a comparison between color parameters of the pixels making up the valid tissue zone and reference color characteristics. A pixel within a valid tissue zone may be categorized, or marked, as an “anomalous pixel” or as a “normal” (e.g., regular, or healthy) pixel based on the comparison result, and a region may be regarded as an “anomaly region” if the region's pixels or a group of pixels satisfy a predetermined anomaly criteria or property, such as density of anomalous pixels, percentage of anomalous pixels, or other criteria.
Systems and methods are needed which are not limited to only detecting red pathologies, or requiring image areas whose boundaries can be detected by using high color gradients. Furthermore, systems and methods are needed, which may update thresholds associated with the systems and methods using images in new image streams. Such new systems and methods are needed to avoid the triggering of many false alarms, for example. As such, new solutions are needed which resolve these and other limitations in existing systems and methods.